OpenRaiser/Intern-Atlas huggingface dataset:
Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships
Venue: arXiv cs.IR 2018
Year: 2018

Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Communities offer an opportunity for sharing knowledge at a low cost, where communities users, many of whom are domain experts, can potentially provide high-quality solutions to a given problem. In this paper, we propose a framework for finding experts across multiple collaborative networks. We employ the recent techniques of tree-guided learning (via tensor decomposition), and matrix factorization to explore user expertise from past voted posts. Tensor decomposition enables to leverage the latent expertise of users, and the posts and related tags help identify the related areas. The final result is an expertise score for every user on every knowledge area. We experiment on Stack Exchange Networks, a set of question answering websites on different topics with a huge group of users and posts. Experiments show our proposed approach produces steady and premium outputs.
Efficient texture retrieval using multiscale local extrema descriptors and covariance embedding
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents an efficient method for texture retrieval using multiscale feature extraction and embedding based on the local extrema keypoints. The idea is to first represent each texture image by its local maximum and local minimum pixels. The image is then divided into regular overlapping blocks and each one is characterized by a feature vector constructed from the radiometric, geometric and structural information of its local extrema. All feature vectors are finally embedded into a covariance matrix which will be exploited for dissimilarity measurement within retrieval task. Thanks to the method's simplicity, multiscale scheme can be easily implemented to improve its scale-space representation capacity. We argue that our handcrafted features are easy to implement, fast to run but can provide very competitive performance compared to handcrafted and CNN-based learned descriptors from the literature. In particular, the proposed framework provides highly competitive retrieval rate for several texture databases including 94.95% for MIT Vistex, 79.87% for Stex, 76.15% for Outex TC-00013 and 89.74% for USPtex.
Hoeffding Trees with nmin adaptation
Venue: arXiv cs.LG 2018
Year: 2018

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient. In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin parameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.
A Two-Dimensional (2-D) Learning Framework for Particle Swarm based Feature Selection
Venue: arXiv cs.NE 2018
Year: 2018

This paper proposes a new generalized two dimensional learning approach for particle swarm based feature selection. The core idea of the proposed approach is to include the information about the subset cardinality into the learning framework by extending the dimension of the velocity. The 2D-learning framework retains all the key features of the original PSO, despite the extra learning dimension. Most of the popular variants of PSO can easily be adapted into this 2D learning framework for feature selection problems. The efficacy of the proposed learning approach has been evaluated considering several benchmark data and two induction algorithms: Naive-Bayes and k-Nearest Neighbor. The results of the comparative investigation including the time-complexity analysis with GA, ACO and five other PSO variants illustrate that the proposed 2D learning approach gives feature subset with relatively smaller cardinality and better classification performance with shorter run times.
ViPIOS - VIenna Parallel Input Output System: Language, Compiler and Advanced Data Structure Support for Parallel I/O Operations
Venue: arXiv cs.DC 2018
Year: 2018

For an increasing number of data intensive scientific applications, parallel I/O concepts are a major performance issue. Tackling this issue, we develop an input/output system designed for highly efficient, scalable and conveniently usable parallel I/O on distributed memory systems. The main focus of this research is the parallel I/O runtime system support provided for software-generated programs produced by parallelizing compilers in the context of High Performance FORTRAN efforts. Specifically, our design aims for the Vienna Fortran Compilation System. In our research project we investigate the I/O problem from a runtime system support perspective. We focus on the design of an advanced parallel I/O support, called ViPIOS (VIenna Parallel I/O System), to be targeted by language compilers supporting the same programming model like High Performance Fortran (HPF). The ViPIOS design is partly influenced by the concepts of parallel database technology. At the beginning of the project we developed a formal model, which forms a theoretical framework on which the ViPIOS design is based. This model describes the mapping of the problem specific data space starting from the application program data structures down to the physical layout on disk across several intermediate representation levels. Based on this formal model we designed and developed an I/O runtime system, ViPIOS, which provides support for several issues, as - parallel access to files for read/write operations, - optimization of data-layout on disks, - redistribution of data stored on disks, - communication of out-of-core (OOC) data, and - many optimizations including data prefetching from disks based on the access pattern knowledge extracted from the program by the compiler or provided by a user specification.
Adversarial Coordination on Social Networks
Venue: arXiv cs.SI 2018
Year: 2018

Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue which has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present and players can communicate. Finally, we use experimental data to develop a computational model of human behavior, and explore a number of additional parametric variations, such as features of network topologies, using the resulting data-driven agent-based model.
An SDN-based Approach For Defending Against Reflective DDoS Attacks
Venue: arXiv cs.CR 2018
Year: 2018

Distributed Reflective Denial of Service (DRDoS) attacks are an immanent threat to Internet services. The potential scale of such attacks became apparent in March 2018 when a memcached-based attack peaked at 1.7 Tbps. Novel services built upon UDP increase the need for automated mitigation mechanisms that react to attacks without prior knowledge of the actual application protocols used. With the flexibility that software-defined networks offer, we developed a new approach for defending against DRDoS attacks; it not only protects against arbitrary DRDoS attacks but is also transparent for the attack target and can be used without assistance of the target host operator. The approach provides a robust mitigation system which is protocol-agnostic and effective in the defense against DRDoS attacks.
Enabling Trust in Deep Learning Models: A Digital Forensics Case Study
Venue: arXiv cs.CR 2018
Year: 2018

Today, the volume of evidence collected per case is growing exponentially, to address this problem forensics investigators are looking for investigation process with tools built on new technologies like big data, cloud services, and Deep Learning (DL) techniques. Consequently, the accuracy of artifacts found also relies on the performance of techniques used, especially DL models. Recently, \textbf{D}eep \textbf{N}eural \textbf{N}ets (\textbf{DNN}) have achieved state of the art performance in the tasks of classification and recognition. In the context of digital forensics, DNN has been applied to the domains of cybercrime investigation such as child abuse investigations, malware classification, steganalysis and image forensics. However, the robustness of DNN models in the context of digital forensics is never studied before. Hence, in this research, we design and implement a domain-independent Adversary Testing Framework (ATF) to test the security robustness of black-box DNN's. By using ATF, we also methodically test a commercially available DNN service used in forensic investigations and bypass the detection, where published methods fail in control settings.
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling
Venue: arXiv cs.CV 2018
Year: 2018

Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image / video processing applications. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using either approach alone usually limits performance in image reconstruction or recovery applications. In this work, we propose a simultaneous sparsity and low-rank model, dubbed STROLLR, to better represent natural images. In order to fully utilize both the local and non-local image properties, we develop an image restoration framework using a transform learning scheme with joint low-rank regularization. The approach owes some of its computational efficiency and good performance to the use of transform learning for adaptive sparse representation rather than the popular synthesis dictionary learning algorithms, which involve approximation of NP-hard sparse coding and expensive learning steps. We demonstrate the proposed framework in various applications to image denoising, inpainting, and compressed sensing based magnetic resonance imaging. Results show promising performance compared to state-of-the-art competing methods.
Purely Geometric Scene Association and Retrieval - A Case for Macro Scale 3D Geometry
Venue: arXiv cs.CV 2018
Year: 2018

We address the problems of measuring geometric similarity between 3D scenes, represented through point clouds or range data frames, and associating them. Our approach leverages macro-scale 3D structural geometry - the relative configuration of arbitrary surfaces and relationships among structures that are potentially far apart. We express such discriminative information in a viewpoint-invariant feature space. These are subsequently encoded in a frame-level signature that can be utilized to measure geometric similarity. Such a characterization is robust to noise, incomplete and partially overlapping data besides viewpoint changes. We show how it can be employed to select a diverse set of data frames which have structurally similar content, and how to validate whether views with similar geometric content are from the same scene. The problem is formulated as one of general purpose retrieval from an unannotated, spatio-temporally unordered database. Empirical analysis indicates that the presented approach thoroughly outperforms baselines on depth / range data. Its depth-only performance is competitive with state-of-the-art approaches with RGB or RGB-D inputs, including ones based on deep learning. Experiments show retrieval performance to hold up well with much sparser databases, which is indicative of the approach's robustness. The approach generalized well - it did not require dataset specific training, and scaled up in our experiments. Finally, we also demonstrate how geometrically diverse selection of views can result in richer 3D reconstructions.
CT-Wasm: Type-Driven Secure Cryptography for the Web Ecosystem
Venue: arXiv cs.CR 2018
Year: 2018

A significant amount of both client and server-side cryptography is implemented in JavaScript. Despite widespread concerns about its security, no other language has been able to match the convenience that comes from its ubiquitous support on the "web ecosystem" - the wide variety of technologies that collectively underpins the modern World Wide Web. With the new introduction of the WebAssembly bytecode language (Wasm) into the web ecosystem, we have a unique opportunity to advance a principled alternative to existing JavaScript cryptography use cases which does not compromise this convenience. We present Constant-Time WebAssembly (CT-Wasm), a type-driven strict extension to WebAssembly which facilitates the verifiably secure implementation of cryptographic algorithms. CT-Wasm's type system ensures that code written in CT-Wasm is both information flow secure and resistant to timing side channel attacks; like base Wasm, these guarantees are verifiable in linear time. Building on an existing Wasm mechanization, we mechanize the full CT-Wasm specification, prove soundness of the extended type system, implement a verified type checker, and give several proofs of the language's security properties. We provide two implementations of CT-Wasm: an OCaml reference interpreter and a native implementation for Node.js and Chromium that extends Google's V8 engine. We also implement a CT-Wasm to Wasm rewrite tool that allows developers to reap the benefits of CT-Wasm's type system today, while developing cryptographic algorithms for base Wasm environments. We evaluate the language, our implementations, and supporting tools by porting several cryptographic primitives - Salsa20, SHA-256, and TEA - and the full TweetNaCl library. We find that CT-Wasm is fast, expressive, and generates code that we experimentally measure to be constant-time.
Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventional ZSL methods have equally dealt with all the available attributes, but this sometimes causes misclassification. This is because an attribute that is effective for classifying instances of one class is not always effective for another class. In this case, a metric of classifying the latter class can be undesirably influenced by the irrelevant attribute. This paper solves this problem by taking the importance of each attribute for each class into account when calculating the metric. In addition to the proposal of this new method, this paper also contributes by providing a dataset for pose classification based on wearable sensors, named HDPoseDS. It contains 22 classes of poses performed by 10 subjects with 31 IMU sensors across full body. To the best of our knowledge, it is the richest wearable-sensor dataset especially in terms of sensor density, and thus it is suitable for studying zero-shot pose/action recognition. The presented method was evaluated on HDPoseDS and outperformed relative improvement of 5.9% in comparison to the best baseline method.
GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware
Venue: arXiv cs.NE 2018
Year: 2018

Modern deep learning systems rely on (a) a hand-tuned neural network topology, (b) massive amounts of labeled training data, and (c) extensive training over large-scale compute resources to build a system that can perform efficient image classification or speech recognition. Unfortunately, we are still far away from implementing adaptive general purpose intelligent systems which would need to learn autonomously in unknown environments and may not have access to some or any of these three components. Reinforcement learning and evolutionary algorithm (EA) based methods circumvent this problem by continuously interacting with the environment and updating the models based on obtained rewards. However, deploying these algorithms on ubiquitous autonomous agents at the edge (robots/drones) demands extremely high energy-efficiency due to (i) tight power and energy budgets, (ii) continuous/lifelong interaction with the environment, (iii) intermittent or no connectivity to the cloud to run heavy-weight processing. To address this need, we present GENESYS, an HW-SW prototype of an EA-based learning system, that comprises a closed loop learning engine called EvE and an inference engine called ADAM. EvE can evolve the topology and weights of neural networks completely in hardware for the task at hand, without requiring hand-optimization or backpropagation training. ADAM continuously interacts with the environment and is optimized for efficiently running the irregular neural networks generated by EvE. GENESYS identifies and leverages multiple unique avenues of parallelism unique to EAs that we term 'gene'- level parallelism, and 'population'-level parallelism. We ran GENESYS with a suite of environments from OpenAI gym and observed 2-5 orders of magnitude higher energy-efficiency over state-of-the-art embedded and desktop CPU and GPU systems.
Large Scale Language Modeling: Converging on 40GB of Text in Four Hours
Venue: arXiv cs.LG 2018
Year: 2018

Recent work has shown how to train Convolutional Neural Networks (CNNs) rapidly on large image datasets, then transfer the knowledge gained from these models to a variety of tasks. Following [Radford 2017], in this work, we demonstrate similar scalability and transfer for Recurrent Neural Networks (RNNs) for Natural Language tasks. By utilizing mixed precision arithmetic and a 32k batch size distributed across 128 NVIDIA Tesla V100 GPUs, we are able to train a character-level 4096-dimension multiplicative LSTM (mLSTM) for unsupervised text reconstruction over 3 epochs of the 40 GB Amazon Reviews dataset in four hours. This runtime compares favorably with previous work taking one month to train the same size and configuration for one epoch over the same dataset. Converging large batch RNN models can be challenging. Recent work has suggested scaling the learning rate as a function of batch size, but we find that simply scaling the learning rate as a function of batch size leads either to significantly worse convergence or immediate divergence for this problem. We provide a learning rate schedule that allows our model to converge with a 32k batch size. Since our model converges over the Amazon Reviews dataset in hours, and our compute requirement of 128 Tesla V100 GPUs, while substantial, is commercially available, this work opens up large scale unsupervised NLP training to most commercial applications and deep learning researchers. A model can be trained over most public or private text datasets overnight.
ReproServer: Making Reproducibility Easier and Less Intensive
Venue: arXiv cs.SE 2018
Year: 2018

Reproducibility in the computational sciences has been stymied because of the complex and rapidly changing computational environments in which modern research takes place. While many will espouse reproducibility as a value, the challenge of making it happen (both for themselves and testing the reproducibility of others' work) often outweigh the benefits. There have been a few reproducibility solutions designed and implemented by the community. In particular, the authors are contributors to ReproZip, a tool to enable computational reproducibility by tracing and bundling together research in the environment in which it takes place (e.g. one's computer or server). In this white paper, we introduce a tool for unpacking ReproZip bundles in the cloud, ReproServer. ReproServer takes an uploaded ReproZip bundle (.rpz file) or a link to a ReproZip bundle, and users can then unpack them in the cloud via their browser, allowing them to reproduce colleagues' work without having to install anything locally. This will help lower the barrier to reproducing others' work, which will aid reviewers in verifying the claims made in papers and reusing previously published research.
Language Model Supervision for Handwriting Recognition Model Adaptation
Venue: arXiv cs.CV 2018
Year: 2018

Training state-of-the-art offline handwriting recognition (HWR) models requires large labeled datasets, but unfortunately such datasets are not available in all languages and domains due to the high cost of manual labeling.We address this problem by showing how high resource languages can be leveraged to help train models for low resource languages.We propose a transfer learning methodology where we adapt HWR models trained on a source language to a target language that uses the same writing script.This methodology only requires labeled data in the source language, unlabeled data in the target language, and a language model of the target language. The language model is used in a bootstrapping fashion to refine predictions in the target language for use as ground truth in training the model.Using this approach we demonstrate improved transferability among French, English, and Spanish languages using both historical and modern handwriting datasets. In the best case, transferring with the proposed methodology results in character error rates nearly as good as full supervised training.
Learning to Align Images using Weak Geometric Supervision
Venue: arXiv cs.CV 2018
Year: 2018

Image alignment tasks require accurate pixel correspondences, which are usually recovered by matching local feature descriptors. Such descriptors are often derived using supervised learning on existing datasets with ground truth correspondences. However, the cost of creating such datasets is usually prohibitive. In this paper, we propose a new approach to align two images related by an unknown 2D homography where the local descriptor is learned from scratch from the images and the homography is estimated simultaneously. Our key insight is that a siamese convolutional neural network can be trained jointly while iteratively updating the homography parameters by optimizing a single loss function. Our method is currently weakly supervised because the input images need to be roughly aligned. We have used this method to align images of different modalities such as RGB and near-infra-red (NIR) without using any prior labeled data. Images automatically aligned by our method were then used to train descriptors that generalize to new images. We also evaluated our method on RGB images. On the HPatches benchmark, our method achieves comparable accuracy to deep local descriptors that were trained offline in a supervised setting.